
import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    int          =			["""image_processor""", """tokenizer"""]
			__UpperCamelCase   :    Optional[int]          =			"""BlipImageProcessor"""
			__UpperCamelCase   :    Union[str, Any]          =			"""AutoTokenizer"""

			def __init__(							self			:							Any    ,						snake_case_			:							Tuple    ,						snake_case_			:							Tuple    ,						snake_case_			:							str   ):
							super().__init__(snake_case_    ,						snake_case_   )

							# add QFormer tokenizer
							UpperCamelCase_:				Optional[Any]						=    qformer_tokenizer

			def __call__(							self			:							int    ,						snake_case_			:							ImageInput = None    ,						snake_case_			:							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,						snake_case_			:							bool = True    ,						snake_case_			:							Union[bool, str, PaddingStrategy] = False    ,						snake_case_			:							Union[bool, str, TruncationStrategy] = None    ,						snake_case_			:							Optional[int] = None    ,						snake_case_			:							int = 0    ,						snake_case_			:							Optional[int] = None    ,						snake_case_			:							Optional[bool] = None    ,						snake_case_			:							bool = False    ,						snake_case_			:							bool = False    ,						snake_case_			:							bool = False    ,						snake_case_			:							bool = False    ,						snake_case_			:							bool = False    ,						snake_case_			:							bool = True    ,						snake_case_			:							Optional[Union[str, TensorType]] = None    ,						**snake_case_			:							Any    ,						):
							if images is None and text is None:
											raise ValueError("""You have to specify at least images or text."""   )

							UpperCamelCase_:				Optional[Any]						=    BatchFeature()

							if text is not None:
											UpperCamelCase_:				int						=    self.tokenizer(
											    text=snake_case_    ,						add_special_tokens=snake_case_    ,						padding=snake_case_    ,						truncation=snake_case_    ,						max_length=snake_case_    ,						stride=snake_case_    ,						pad_to_multiple_of=snake_case_    ,						return_attention_mask=snake_case_    ,						return_overflowing_tokens=snake_case_    ,						return_special_tokens_mask=snake_case_    ,						return_offsets_mapping=snake_case_    ,						return_token_type_ids=snake_case_    ,						return_length=snake_case_    ,						verbose=snake_case_    ,						return_tensors=snake_case_    ,						**snake_case_    ,						)
											encoding.update(snake_case_   )
											UpperCamelCase_:				Optional[Any]						=    self.qformer_tokenizer(
											    text=snake_case_    ,						add_special_tokens=snake_case_    ,						padding=snake_case_    ,						truncation=snake_case_    ,						max_length=snake_case_    ,						stride=snake_case_    ,						pad_to_multiple_of=snake_case_    ,						return_attention_mask=snake_case_    ,						return_overflowing_tokens=snake_case_    ,						return_special_tokens_mask=snake_case_    ,						return_offsets_mapping=snake_case_    ,						return_token_type_ids=snake_case_    ,						return_length=snake_case_    ,						verbose=snake_case_    ,						return_tensors=snake_case_    ,						**snake_case_    ,						)
											UpperCamelCase_:				str						=    qformer_text_encoding.pop("""input_ids"""   )
											UpperCamelCase_:				str						=    qformer_text_encoding.pop("""attention_mask"""   )

							if images is not None:
											UpperCamelCase_:				Union[str, Any]						=    self.image_processor(snake_case_    ,						return_tensors=snake_case_   )
											encoding.update(snake_case_   )

							return encoding

			def    lowerCAmelCase__						(							self			:							Tuple    ,						*snake_case_			:							Optional[int]    ,						**snake_case_			:							Dict   ):
							return self.tokenizer.batch_decode(*snake_case_    ,						**snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						*snake_case_			:							int    ,						**snake_case_			:							Any   ):
							return self.tokenizer.decode(*snake_case_    ,						**snake_case_   )

			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_:				Optional[Any]						=    self.tokenizer.model_input_names
							UpperCamelCase_:				Optional[Any]						=    self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )

			def    lowerCAmelCase__						(							self			:							List[Any]    ,						snake_case_			:							Union[str, Any]    ,						**snake_case_			:							Optional[Any]   ):
							if os.path.isfile(snake_case_   ):
											raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''   )
							os.makedirs(snake_case_    ,						exist_ok=snake_case_   )
							UpperCamelCase_:				Any						=    os.path.join(snake_case_    ,						"""qformer_tokenizer"""   )
							self.qformer_tokenizer.save_pretrained(snake_case_   )
							return super().save_pretrained(snake_case_    ,						**snake_case_   )




			@classmethod
			def    lowerCAmelCase__						(							cls			:							List[str]    ,						snake_case_			:							Optional[Any]    ,						**snake_case_			:							List[str]   ):
							UpperCamelCase_:				List[Any]						=    AutoTokenizer.from_pretrained(snake_case_    ,						subfolder="""qformer_tokenizer"""   )
							UpperCamelCase_:				int						=    cls._get_arguments_from_pretrained(snake_case_    ,						**snake_case_   )
							args.append(snake_case_   )
							return cls(*snake_case_   )








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
							import torch

							from transformers.generation import DisjunctiveConstraint

@require_torch
class 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			def    lowerCAmelCase__						(							self			:							Dict   ):
							# For consistency across different places the DisjunctiveConstraint is called,
							# dc.token_ids is a list of integers. It is also initialized only by integers.

							UpperCamelCase_:				Optional[int]						=    [[1, 2, 4], [1, 2, 3, 4]]
							UpperCamelCase_:				List[str]						=    DisjunctiveConstraint(snake_case_   )
							self.assertTrue(isinstance(dc.token_ids    ,						snake_case_   )   )

							with self.assertRaises(snake_case_   ):
											DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

							with self.assertRaises(snake_case_   ):
											DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							# We can't have constraints that are complete subsets of another. This leads to a preverse
							# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
							# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
							# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
							# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
							UpperCamelCase_:				Dict						=    [[1, 2], [1, 2, 3, 4]]

							with self.assertRaises(snake_case_   ):
											DisjunctiveConstraint(snake_case_   )  # fails here

			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Dict						=    [[1, 2, 3], [1, 2, 4]]

							UpperCamelCase_:				Dict						=    DisjunctiveConstraint(snake_case_   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				str						=    dc.update(1   )
							UpperCamelCase_:				Union[str, Any]						=    stepped is True and completed is False and reset is False
							self.assertTrue(snake_case_   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    dc.update(2   )
							UpperCamelCase_:				Tuple						=    stepped is True and completed is False and reset is False
							self.assertTrue(snake_case_   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1, 2]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    dc.update(3   )
							UpperCamelCase_:				Union[str, Any]						=    stepped is True and completed is True and reset is False
							self.assertTrue(snake_case_   )
							self.assertTrue(dc.completed   )  # Completed!
							self.assertTrue(dc.current_seq == [1, 2, 3]   )




			def    lowerCAmelCase__						(							self			:							List[str]   ):
							UpperCamelCase_:				Optional[int]						=    [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

							UpperCamelCase_:				Tuple						=    DisjunctiveConstraint(snake_case_   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Dict						=    dc.update(1   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Union[str, Any]						=    dc.update(2   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1, 2]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Any						=    dc.update(4   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1, 2, 4]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				int						=    dc.update(5   )
							self.assertTrue(dc.completed   )  # Completed!
							self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

							dc.reset()

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				int						=    dc.update(1   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.remaining() == 3   )
							self.assertTrue(dc.current_seq == [1]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Tuple						=    dc.update(2   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.remaining() == 2   )
							self.assertTrue(dc.current_seq == [1, 2]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				List[str]						=    dc.update(5   )
							self.assertTrue(dc.completed   )  # Completed!
							self.assertTrue(dc.remaining() == 0   )
							self.assertTrue(dc.current_seq == [1, 2, 5]   )






